Recent Trends in Measures to Manage Capital Flows in Emerging Economies
Bibliographic record
Abstract
This paper reviews recent trends in the imposition of capital flow management measures in emerging market economies (EMEs). We find that since the crisis, there has been a shift in the balance of new measures towards net capital inflow reducing measures. However, this is not driven primarily by an increase in inflow tightening measures (e.g. taxes on inflows), but rather by significantly slower inflow liberalization trends (i.e. existing capital controls remaining in place). In addition, there has been a compositional shift in net capital inflow reducing measures: outflow liberalizations were the predominant tools for reducing net capital inflows pre-crisis, but such measures have become less important post-crisis. Overall, the pre-crisis trend towards capital account openness has stalled. The use of capital flow management measures is motivated by macroeconomic as well as financial stability concerns. The IMF recently endorsed use of capital controls as “last resort ” measures in macroeconomic management. We also find that by IMF criteria, capital flow measures have not been introduced as a last resort since 2004-alternative macroeconomic policies to deal with the surge in net capital inflows were available to the majority of countries. Moreover, most capital flow measures introduced by EMEs since 2004 are pure capital controls rather than currency based and/or prudential type measures, suggesting that they were not
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".